A feed-forward neural net with backpropagation has proved to be a better predictor of economic forecasts than traditional statistics-based systems. A market price of a stock can be predicted by training the net on its price data in the past several months along with relevant economic parameters. ID3 extension, trained on the same data, can give explanations, based on the classifications it makes. The latter of the two similarity-based reasoners, i.e., case- based reasoner and Grossberg net, if adapting its vigilance parameter appropriately, can adequately classify a newly coming data in a dynamically changing environment, keeping a history of stock price transition. However, the economic world is undergoing neverending changes, under the competition of several antagonistic factors, which are modeled using rules that form a subset of rule-based subcomponent of the case-based reasoner, which are triggered when the system judges that the arriving case is not in the past experience. By reviewing predictions and realization the genetic algorithm system, applying its reproduction, crossover, and mutation operators, adapts the configuration of backprop/ID3 and that of Grossberg net along with its vigilance parameter, and evolves the economic rules, so that the CBR can do as much stable work as possible for some time, based on its past experience into which the newly arrived cases have just been integrated. CBR can give various sorts of useful explanations and can be used to construct extended works, such as portfolio organization, for example, on top of the prediction and history the system has experienced.